MEETING-DATE:Jan 7, 2016
MEETING-LOCATION:DC 1331
MEETING-TIME:11:30
MEETING-CHAIR:Christopher Batty
MEETING-CHAIRPIC:batty.jpg
COFFEE-HOUR-LAST-WEEK:Volunteers?
COFFEE-HOUR-THIS-WEEK:Volunteers?
COFFEE-HOUR-NEXT-WEEK:Christopher!
FORTH-DATE1:Jan 14, 2015
FORTH-DATE2:Jan 21, 2015
FORTH-DATE3:Jan 28, 2015
FORTH-DATE4:Feb 4, 2016
FORTH-LOCATION1:DC 1331 11:30
FORTH-LOCATION2:DC 1331 11:30
FORTH-LOCATION3:DC 1331 11:30
FORTH-LOCATION4:DC 1331 11:30
FORTH-CHAIR1:Bill Cowan
FORTH-CHAIR2:Ryan Goldade
FORTH-CHAIR3:Craig Kaplan
FORTH-CHAIR4:Marta Kryven
FORTH-CHAIRPIC1:bill.png
FORTH-CHAIRPIC2:ryan.jpg
FORTH-CHAIRPIC3:craig.jpg
FORTH-CHAIRPIC4:marta.jpg
FORTH-TP1:Christopher Batty
FORTH-TP2:Bill Cowan
FORTH-TP3:Ryan Goldade
FORTH-TP4:Craig Kaplan
FORTH-TPPIC1:batty.jpg
FORTH-TPPIC2:bill.png
FORTH-TPPIC3:ryan.jpg
FORTH-TPPIC4:craig.jpg
TPNAME:Yipeng Wang (maybe)
TPTITLE:Data-driven fluid simulations using regression forests
TPABSTRACT:Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, real time fluid simulations have been possible only under very restricted conditions. In this paper the authors propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. They designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos.
TPPIC:yipeng.jpg
DIONE:The meeting scripts are still painfully slow. Is there any resolution in sight? Funny you should ask<nl>they seem to be fine now.<nl>   <nl>
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LEONE:The Waterloo SIGGRAPH Student Chapter has booked an employer panel for Jan 19 5-7PM in Fed Hall.  SideEffects, Autodesk, Christie Digital, AMD, and EA Games will all be there.
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SEMINARS:<DT><A HREF="https://www.math.uwaterloo.ca/~wnotice/notice_prgms/wreg/list_full.pl?20160008-0200.18690">Friday, 8 January 2016</A>, 2:00AM - Computer Science  (<em>Networks and Distributed Systems</em>), DC 2310 <nl><DD> Joseph Mate: -- <EM> Fault-Tolerant Database Server Consolidation </EM><BR> <BR> <nl><DT><A HREF="https://www.math.uwaterloo.ca/~wnotice/notice_prgms/wreg/list_full.pl?20160011-1000.3711">Monday, 11 January 2016</A>, 10:00AM - Applied Mathematics  , M3-2134 <nl><DD> Manda Winlaw: -- <EM> Algorithms and Models for Tensors and Networks with Applications in Data Science </EM><BR> <BR> <nl><DT><A HREF="https://www.math.uwaterloo.ca/~wnotice/notice_prgms/wreg/list_full.pl?20160011-1030.8662">Monday, 11 January 2016</A>, 10:30AM - Computer Science  , DC 1304<nl><DD> Dr. Andrew Wilson: -- <EM> Scalable Gaussian Processes for Scientific Discovery </EM><BR> <BR> <nl><DT><A HREF="https://www.math.uwaterloo.ca/~wnotice/notice_prgms/wreg/list_full.pl?20160011-1400.26786">Monday, 11 January 2016</A>, 2:00PM - Computer Science  (<em>Artificial Intelligence Lab</em>), DC 2306C <nl><DD> Shenyang Pan: -- <EM> Dynamic Crowdsourcing Consensus Tasks with Workers That Can Learn </EM><BR> <BR> <nl><DT><A HREF="https://www.math.uwaterloo.ca/~wnotice/notice_prgms/wreg/list_full.pl?20160011-1430.29372">Monday, 11 January 2016</A>, 2:30PM - Computational Mathematics  , MC5479 <nl><DD> Dr. Chen Greif: -- <EM> The (Numerical) Linear Algebra Behind Solving Problems with Constraints </EM><BR> <BR>  <nl><DT><A HREF="https://www.math.uwaterloo.ca/~wnotice/notice_prgms/wreg/list_full.pl?20160014-1030.9150">Thursday, 14 January 2016</A>, 10:30AM - Computer Science  , DC 1304 <nl><DD> Dr. Alexander Schwing: -- <EM> Parallel Inference and Learning with Deep Structured Distributions </EM><BR> <BR> <nl>